Operational S2S Strategy: From Training to Live Forecasting

The Challenge

You plan to train your model using ERA5 (Reanalysis) as inputs ($X$) and CHIRPS as targets ($Y$).

• ERA5 is perfect for training because it is consistent and historical (1980-Present).
• BUT: ERA5 is NOT available in real-time. It has a latency of ~5 days for the preliminary version (ERA5T) and months for the quality-controlled version.

To make this operational, you need Real-Time Inputs that "look like" ERA5 but are available today.

Option 1: The "Direct S2S" Approach (Pure AI Forecast)

Your model acts as the simulator. It takes Today's State $\to$ Predicts Week 3-4.

1. Atmospheric Inputs (Wind, Geopotential, Humidity)

Since ERA5 is produced by ECMWF, the best operational proxy is the ECMWF Operational Analysis (IFS).

• Source: ECMWF Open Data or commercial license.
• Alternative (Free): GFS Analysis (NCEP).
  • Risk: GFS data looks slightly different from ERA5. This "Domain Shift" can confuse a Neural Network.
  • Solution: If you use GFS for operations, you should ideally train on GFS Reforecasts or fine-tune your ERA5-trained model on GFS data.

2. Ocean Inputs (SST)

The ocean changes slowly, so having the absolute latest hour is less critical, but you still need daily updates.

• Operational Source: NOAA OISST v2 (Optimum Interpolation SST).
  • Availability: 1-day delay (Near Real-Time).
  • Consistency: OISST is often used to drive reanalyses, so the consistency with ERA5 SST is generally high.

Option 2: The "Hybrid" Approach (Model Output Statistics / Downscaling)

Your model corrects a Physical Model. Input is a coarse Physical Forecast $\to$ Predicts High-Res Reality.

In this scenario, you do not train on ERA5. Instead, you train on Reforecasts (Hindcasts) from a dynamical model.

• Training Input ($X$): ECMWF S2S Reforecasts (past 20 years).
• Training Target ($Y$): CHIRPS.
• Operational Input: Live ECMWF S2S Forecast.

Verdict: Since you specifically asked about Training on ERA5, you are likely following Option 1 (Direct S2S).

Recommended Operational Pipeline (for Option 1)

Handling the "Lag" (The 5-day Gap)

If you use ERA5T (5-day delay) for operations:

• Your operational forecast for "Week 3" will actually be a forecast for "Week 3, starting 5 days ago".
• This effectively means you are doing a Lead 25-day forecast to get a "Week 3" prediction relative to today.
• Recommendation: For S2S (where lead times are long), a 5-day data lag is often acceptable. Using ERA5T is the safest bet for maintaining model accuracy because it comes from the exact same system as your training data.

Summary Checklist for Operations

1. Download ERA5T: Set up a cron job to fetch the latest preliminary ERA5 data (last month).
2. Download OISST: Fetch the daily SST update.
3. Preprocessing: Apply the exact same normalization (Mean/Std) you saved during training. Do not re-calculate statistics on the live data.
4. Inference: Run the U-Net/cGAN.